Disclaimer: This file was written with assistance from AI-based software other than CS50's own. (Chat GPT)

This project is a comprehensive, automated solution for monitoring and maintaining optimal conditions for plant growth. It integrates various sensors, hardware controls, and a web-based interface to track environmental factors like soil moisture, temperature, humidity, and water tank levels. The system is designed to automate the watering process, ensuring that plants receive the right amount of water based on real-time data.

The equipment used in this project:

• Raspberry Pi 3B+
• 5V submersible water pump
• HC-SR04 Ultrasonic distance sensor
• DHT11 Temperature and relative humidity sensor
• A basic breadboard, dupont jumper wires and LED diodes
• Arduino Uno
• 3.3-5V capacitive soil moisture sensor
• LEDlife Max-Grow 30W grow light

This script is an automated plant monitoring and watering system that integrates various sensors and hardware controls to maintain optimal plant conditions.

External libraries:

• Adafruit Blinka
• Adafruit CircuitPython DHT
• Adafruit CircuitPython HCSR04
• pyfirmata2

As the Raspberry Pi lacks built-in analog-to-digital conversion, an Arduino is acting as an intermediary between the moisture sensor and the Raspberry Pi. It is used to convert the analog signals from the soil moisture sensor into digital data that can be processed by the Raspberry Pi.

The take_moisture_sample() function collects analog readings from the moisture sensor. The readings are averaged, inverted, and normalized to provide a moisture value where a high number indicates high moisture.

The take_temperature_and_humidiy_sample() function reads temperature and humidity data using an Adafruit DHT11 sensor. Multiple readings are taken to ensure accuracy, and errors are handled to avoid interruptions.

The take_tank_level_sample() function uses an ultrasonic sensor (HC-SR04) to measure the distance to the water surface in the tank, giving an indication of the water level.

The take_samples() function aggregates the moisture, temperature, humidity, and tank level data. It then stores these measurements in a SQLite database, recording the environmental conditions for historical analysis.

The water_plant() function determines whether the plant requires watering based on the moisture level compared to predefined thresholds stored in the database. If the soil is too dry and the water tank has sufficient water, the script activates a water pump via a GPIO pin on the Raspberry Pi to water the plant. The action is logged in the database for tracking purposes.

The script runs in an infinite loop, executing the take_samples() and water_plant() functions every 15 minutes. It continuously monitors and maintains plant conditions

Certainly! Below is the markdown to include in your README.md for the files api.py, view.html, and header.html, following the structure you've used for plant_monitoring.py.

This file serves as the backend API, enabling the interaction between the plant monitoring data and the web interface. It is built using FastAPI and integrates with the SQLite database to provide real-time data to the front-end.

• FastAPI
• Jinja2
• SQLite3

• Purpose: Serves the main dashboard that displays plant monitoring data.
• Functionality: Retrieves sensor data from the SQLite database and renders it using the view.html template. Also calculates moisture level boundaries and translates tank level measurements into percentages.

• Purpose: Provides the latest temperature, humidity, and tank level readings.
• Functionality: Fetches the most recent sensor data and updates the dashboard header dynamically using the header.html template.

• Purpose: Supplies real-time moisture data for the dashboard's chart.
• Functionality: Returns JSON-encoded sensor readings that are used to update the moisture level chart on the front-end.

• Purpose: Serves static assets (CSS, images) required for the front-end.
• Functionality: Uses FastAPI’s StaticFiles to deliver assets from the assets directory.

• Utility: Includes functions like translate() to convert raw sensor data into meaningful values, such as converting ultrasonic sensor measurements into tank water level percentages.

This is the main template for the web-based plant monitoring dashboard. It provides a user-friendly interface to visualize real-time and historical data about the plant's environment.

• HTMX
• Chart.js
• Luxon
• Simple.css

• Purpose: Ensures the dashboard is accessible and visually appealing across different devices.
• Functionality: Uses Simple.css for a minimalist design and includes media queries for mobile responsiveness.

• Purpose: Keeps the dashboard data up-to-date without full-page reloads.
• Functionality: Utilizes HTMX to automatically refresh the header and chart data at regular intervals.

• Purpose: Visualizes moisture data over time to help users track the plant's hydration levels.
• Functionality: Implements a time-series line chart using Chart.js and Luxon, which dynamically updates with new data fetched from the /chart-data endpoint.

• Purpose: Displays the latest temperature, humidity, and tank level in a user-friendly format.
• Functionality: Uses material design icons and CSS animations to highlight key environmental metrics.

This template is a component of the main dashboard, focusing on displaying the latest environmental readings in a concise, visually appealing format.

• Purpose: Presents real-time data for key environmental factors.
• Functionality: Fetches the latest readings from the backend and renders them with appropriate icons and styling.

• Purpose: Visually represents the current water level in the tank.
• Functionality: Utilizes CSS animations to adjust the tank level indicator in real-time based on the latest sensor data.

• Purpose: Ensures the header data stays current without requiring manual page refreshes.
• Functionality: Integrated with HTMX to refresh the displayed data every 60 seconds, pulling from the /header endpoint.